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Field
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: • PhD in Geography (Remote Sensing, Geomatics), Computer Science, Agricultural Sciences • Skills and/or knowledge in artificial intelligence (Machine Learning) and programming: proficiency in Python
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for simulating river network dynamics, such as R, Julia, Python, or GIS-based hydrological modeling platforms. Ability to integrate physical, chemical, and biological components into the river-lake network models
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in data analytics and statistical methods, particularly using tools such as R, Python, or other relevant software. Experience with Data Visualization & Programming: Expertise in data visualization
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data Experience with GIS/RS and database environments (e.g., ArcGIS and Quantum GIS) Experience with machine learning and statistical learning Experience working with large, diverse datasets Familiarity
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technologies and methodologies, and Geographic Information Systems software (e.g., ArcGIS, QGIS), Python, and Matlab is required. Knowledge of other programming languages (e.g., C, C++, R, Javascript) is
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to offer. Qualifications: Required: PhD in ecology by start date Experience in plant phenology, biogeography, and spatial and temporal modeling (Bayesian and frequentist) Expertise in R or Python, GIS, big
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expertise in forest ecology, disturbance ecology, and landscape ecology, and methodological expertise in harmonizing distinct databases (e.g., forest inventory, remote sensing, land cover), GIS, and R-based
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remote sensing/climate data. You have programming skills in Python and/or R; you are familiar with reproducible coding and automated geospatial data analysis. You have excellent scientific writing and
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. You have good programming skills in Python and/or R; you are familiar with reproducible coding and automated (geospatial) data analysis. You have excellent scientific writing and communication skills in
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learning methodologies. The underlying data are complex and will require sophisticated data management and integration skills. A candidate should have proficiency with GIS software and Python, strong written